Hierarchical correlation reconstruction | Studencki Festiwal Informatyczny

16th SFI Academic IT Festival

26 - 28 March 2020 | Auditorium Maximum UJ
16 edition 2020

Hierarchical correlation reconstruction

Type: Lecture
Speaker: Jarosław Duda
Day: 23/03/2019 time: 15:15
Class: SFI Enterprise 1.38
Duration: 60 minutes
Language: Polish
SFI Edition: 15

Hierarchical correlation reconstruction - between machine learning and statistics


While machine learning techniques are very powerful, they have some weaknesses, like iterative optimization with many local minimums, large freedom of parameters, lack of their interpretability and accuracy control. From the other side we have classical statistics based on moments not having these issues, but providing only a rough description.

I will talk about an approach which combines their advantages: with (MSE) optimal moment-like coefficients, but designed such that we can directly translate them into probability density. For multivariate case such basis of mixed moments asymptotically allows to accurately reconstruct any joint distribution, each coefficient can be independently and cheaply estimated, has a clear interpretation, and we have some control of its accuracy.

I will also present its two applications: systematic enhancement of ARMA/ARCH-like modeling for any mixed moments and non-stationary time series (https://arxiv.org/pdf/1807.04119), and for credibility evaluation of income data: modeling continuous conditional probability distribution from a large number of variables of various types (https://arxiv.org/abs/1812.08040).

Slides: link